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TASC: Topology Adaptive Spatial Clustering for Sensor Networks Reino Virrankoski, Dimitrios Lymberopoulos and Andreas Savvides Embedded Networks and Application.

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Presentation on theme: "TASC: Topology Adaptive Spatial Clustering for Sensor Networks Reino Virrankoski, Dimitrios Lymberopoulos and Andreas Savvides Embedded Networks and Application."— Presentation transcript:

1 TASC: Topology Adaptive Spatial Clustering for Sensor Networks Reino Virrankoski, Dimitrios Lymberopoulos and Andreas Savvides Embedded Networks and Application Lab Electrical Engineering Department Yale University, New Haven Infocom 2005

2 Ju-Mei Li Outline Introduction TASC  Distributed Leader Election  Discovering Local Network Structure Weight computation  Grouping Similar Densities Density reachability Evaluation Conclusion

3 Ju-Mei Li Introduction A good topology of large-scale sensor networks should help  Sensor nodes coordination  Network management  Data aggregation and compression Goal  Through the development of weights and dynamic density reachablility Topology Adaptive Spatial Clustering Scheme (TASC)

4 Ju-Mei Li TASC: Distributed Leader Election Input information  2-hop neighborhood  Inter-node distance measurements  Min. cluster size  MinPoints Each node uses input information to compute  Weight  Number of density reachable node Midmost position on each shortest path, biggest weight

5 Ju-Mei Li TASC: Distributed Leader Election f g b a c e h d i k j BroadcastToNeighborhood(weight) Select the heaviest density reachable node as nominee BroadcastToNeighborhood(nominee) Select the heaviest density reachable node as nominee BroadcastToNeighborhood(nominee) Density reachable nodes of node i = 4 Density reachable nodes of node j = 7 Density reachable nodes of node k = 3 Select the closest nominee as leader BroadcastToNeighborhood(leaderID, nodeID) Select the closest nominee as leader BroadcastToNeighborhood(leaderID, nodeID)

6 Ju-Mei Li TASC: Distributed Leader Election f g b a c e h d i k j If this node is leader until election timeout; BroadcastToNeighborhood(clustermenbers) If this node is leader until election timeout; BroadcastToNeighborhood(clustermenbers) If clustersize is received If clustersize < min. cluster size = 4 select the closest neighbor for which clustersize ≥ min. cluster size = 4 and joints its cluster BroadcastToNeighborhood(leaderID, clustersize) If clustersize is received If clustersize < min. cluster size = 4 select the closest neighbor for which clustersize ≥ min. cluster size = 4 and joints its cluster BroadcastToNeighborhood(leaderID, clustersize)

7 Ju-Mei Li TASC: Weight computation ABCDE A-B A-B-C A-B-C-D A-B-C-D-E B-C B-C-D B-C-D-E C-D C-D-E D-E

8 Ju-Mei Li TASC: Weight computation Including distance in Weight Computation  If node k is found on path from node i to node j in between node a and node b  Then the weight increment of node k is given A B C DEG F H

9 Ju-Mei Li TASC: Density reachability i Sensing range <= transmission range If MinPoints = m = 3 riri Could be large, equal, or small than sensing range

10 Ju-Mei Li TASC: Density reachability i a b c jk d e Density reachable nodes of node i : node j, node k, node a, node b, and node c

11 Ju-Mei Li TASC: Density reachability i k j i k j

12 Ju-Mei Li TASC: Distributed Leader Election

13 Ju-Mei Li Evaluation PARSEC 100 random scenarios 100 nodes are deployed on 1000*1000 Measurement range  200, 250, 300, 350, 400 Minimum cluster size: 4 Shortest path is done on each node  Floyd-Warshall algorithm

14 Ju-Mei Li Evaluation

15 Ju-Mei Li Evaluation Measurement range: (a)200, (b)300 (a) (b)

16 Ju-Mei Li Evaluation Measurement range: (a)200, (b)300, (c)400

17 Ju-Mei Li Evaluation MinPoints = 2 MinPoints = 4 MinPoints = 6

18 Ju-Mei Li Conclusion This paper proposed a TASC algorithm  Which uses Weight Number of density reachable node  To decompose large network into smaller locally clusters

19 Thank You!!

20 Ju-Mei Li TASC: Density reachability i k j i j k


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